# Modeling of infectious network dynamics for surveillance, control and prevention enhancement (MINDSCAPE)

> **NIH ALLCDC U01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $1,200,000

## Abstract

PROJECT SUMMARY:
Mathematical analysis, computational statistics, and machine learning are increasingly being deployed to
understand and predict the dynamics of healthcare associated infections (HAI) and antimicrobial-resistant
infections (ARI). However, the utility of these models to guiding clinical and health policy decisions often
remains unclear. One challenge is that model calibration quickly becomes obsolete as the epidemiology of HAI
and ARI changes. To address this gap, we propose to use mathematical modeling and machine learning
approaches to build decision-making technologies that improve the risk assessment, prevention, and
control of HAI and ARI. Our proposed technologies account for spatial and temporal dynamics, provide
continuous, real-time feedback to clinicians and are robust to changes in risk factors and disease prevalence
over time. We anticipate that implementation of these technological improvements will help healthcare
institutions to substantially reduce the burden of HAI and ARI. We concentrate our efforts on two of the most
important HAI: methicillin-resistant Staphylococcus aureus and Clostridioides difficile infections. To conduct
these studies, we assembled a team of mathematical modelers, machine learning specialists, health
economists, clinical informaticists, infectious disease physicians, and hospital epidemiologists based in
California, New York, and Texas. Clinical, microbiological and environmental data to train our models will come
from three academic quaternary medical centers and an expanding network of community hospitals. The first
aim is to calculate the patient-specific risk of acquiring or transmitting a HAI or ARI. We hypothesize
that the risk of acquiring an HAI or ARI is more accurately determined when data on patient movement and
pathogen exposure are integrated into predictive models. This type of analysis is also expected to improve the
risk assessment of automated systems used to detect HAI and ARI outbreaks. The second aim is to prevent
invasive methicillin-resistant Staphylococcus aureus (MRSA) infections. One objective is to show that
cost-effective reduction of invasive MRSA infections and hospital-based transmission can be achieved via
personalized decisions for who should be screened for asymptomatic carriage and decolonized. Our third aim
is to control the spread of Clostridioides difficile infections (CDI). We hypothesize that by calculating the
number of CDI averted and the cost saved, models of disease transmission will demonstrate the benefit pre-
emptive adoption of contact precautions for patients who are at high risk of transmitting CDI. We also expect to
identify environmental pathways that contribute to the risk of CDI superspreading and would benefit from
enhanced surveillance and decontamination. Finally, to better understand the importance of antibiotic
stewardship programs, we characterize the specific role a patient's antibiotic, infection, social, exposure and
colonization ...

## Key facts

- **NIH application ID:** 10220762
- **Project number:** 5U01CK000590-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Travis Christian Porco
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2021
- **Award amount:** $1,200,000
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10220762

## Citation

> US National Institutes of Health, RePORTER application 10220762, Modeling of infectious network dynamics for surveillance, control and prevention enhancement (MINDSCAPE) (5U01CK000590-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10220762. Licensed CC0.

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